Feature Importance in Gradient Boosting Models

import pandas as pd
import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Load TSLA dataset
tesla = datasets.load_dataset('codesignal/tsla-historic-prices')
tesla_df = pd.DataFrame(tesla['train'])

# Convert Date column to datetime type
tesla_df['Date'] = pd.to_datetime(tesla_df['Date'])

# Feature Engineering: adding technical indicators as features
tesla_df['SMA_5'] = tesla_df['Adj Close'].rolling(window=5).mean()
tesla_df['SMA_10'] = tesla_df['Adj Close'].rolling(window=10).mean()
tesla_df['EMA_5'] = tesla_df['Adj Close'].ewm(span=5, adjust=False).mean()
tesla_df['EMA_10'] = tesla_df['Adj Close'].ewm(span=10, adjust=False).mean()

# Drop NaN values created by moving averages
tesla_df.dropna(inplace=True)

# Select features and target
features = tesla_df[['Open', 'High', 'Low', 'Close', 'Volume', 'SMA_5', 'SMA_10', 'EMA_5', 'EMA_10']].values
target = tesla_df['Adj Close'].values

# Splitting the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.25, random_state=42)

# Standardizing features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

from sklearn.ensemble import GradientBoostingRegressor

# Instantiate and fit the model
model = GradientBoostingRegressor(random_state=42)
model.fit(X_train, y_train)